In angiographic CT scans, a contrast agent is typically injected into the blood, and three-dimensional data is obtained giving the image density of each voxel in the body or in a region of the body. Typically this data is displayed using a maximum intensity projection (MIP), so that the blood vessels can be examined. However, in a MIP image many blood vessels would be obscured by bones, which have image density comparable to the blood vessels containing contrast agent, so it is desirable to remove the bones from the 3D image before producing the MIP image. This may be accomplished by bone segmentation, i.e. a method to distinguish bone voxels from voxels of other tissue, particularly from blood vessel voxels. Although bone segmentation can be done manually by a doctor examining slices of the 3D image, such a task is very time-consuming and tedious.
Two published US patent applications assigned to General Electric, 2003/0194119 to Manjeshwar et al, and 2003/0113003 to Cline et al, the disclosures of which are incorporated herein by reference, describe semi-automated methods of tissue segmentation. These publications describe the use of single-voxel seeds, chosen by the user as belonging to a body tissue of interest, which are automatically expanded to find the entire connected volume of that tissue, in this case a tumor found by a PET scan, and amyloid plaque found by MRI. General Electric also sells a product called Advantage Windows, which does fully automated segmentation of bone for CT scans. In practice, it is not possible to distinguish bone voxels from blood vessel voxels with 100% accuracy; even two different doctors, attempting to do this task manually, will generally not agree on all voxels.
Alejandro F. Franji, Wiro J. Niessen, Koen L. Vincken, and Max A. Viergever, “Multiscale vessel enhancement filtering,” Lecture Notes in Computer Science 1496, 130 (1998), the disclosure of which is incorporated herein by reference, describes a method of distinguishing blood vessels from other body tissue, using a “vesselness” measure which depends on the ordered eigenvalues of the Hessian matrix of the image density, in either a 2D or 3D image. The vesselness measure is high for long narrow structures such as blood vessels, and low for blob-like or plate-like structures, or background noise.